Col. Drew Cukor, USMC, observed, “Rapidly delivering artificial intelligence to a combat zone won’t be easy.” To address this challenge, the SEI is developing Spiral/AIML: Co-optimization for High-Performance, Data-Intensive Computing in Resource-Constrained Environments.

 

As the military adopts AI/ML to augment human teams, the cost of implementing and re-implementing AI/ML software on new hardware platforms will be prohibitive. To address these challenges, we propose to build on CMU’s Spiral technology, a hardware-software co-optimization system that will

• automatically search and select hardware configurations that meet CSWAP requirements
• automatically generate optimized codes for the selected hardware configuration and the irregular, data-intensive computations required for AI/ML algorithms

If successful, our solution will allow platform developers to realize high-performance AI/ML applications on leading-edge hardware architectures faster and cheaper. These advances will allow for rapid development and deployment of capabilities across the spectrum of national and tactical needs.

In Context

This FY2019-21 project

• builds on DoD line-funded research and sponsored work on automated code generation for future-compatible high-performance graph libraries, big learning benchmarks, GraphBLAS API specification, and graph algorithms on future architectures
• is related to a set of programs at the Defense Advanced Research Projects Administration (DARPA) under the Electronics Resurgence Initiative (ERI) umbrella (Hierarchical Identify Verify Exploit [HIVE], Software Defined Hardware [SDH], Domain Specific System on Chip [DSSoC], etc.) for which the CMU SEI has PWP work
• aligns with the CMU SEI technical objective to be affordable such that the cost of acquisition and operations, despite increased capability, is reduced and predictable and provides a cost advantage over our adversaries